Remote distribution of neural networks

ABSTRACT

Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/888,171, filed on May 29, 2020, which is a continuation of U.S.patent application Ser. No. 15/908,461, filed on Feb. 28, 2018, whichapplications are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present disclosure generally relates to special-purpose machinesthat manage machine learning schemes and improvements to such variants,and to the technologies by which such special-purpose machines becomeimproved compared to other special-purpose machines for networkdistribution of neural networks.

BACKGROUND

Neural networks can be trained for different tasks, such as imageprocessing. A trained neural network model can be transmitted to remoteclient devices for execution. Different client device types (e.g.,different operating systems, screen sizes, processors) often requirecustom-made neural network models that are specifically designed forexecution within a given client device environment. Managing multipleversions of a single neural network model, which then must be sent todifferent client devices over the network when requested, is difficultand often results in a waste of computational resources. Further,sending all client deices all versions is likewise not practical becauseclient devices often have limited memory.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is a block diagram illustrating further details regarding themessaging system of FIG. 1 , according to example embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral), according to some example embodiments.

FIG. 6 shows example functional internal components of a remoteconverter, according to some example embodiments.

FIG. 7 shows an example flow diagram of a method for implementing remotedistribution of neural networks, according to some example embodiments.

FIG. 8 shows an example flow diagram of a method for implementing remotedistribution of neural networks, according to some example embodiments.

FIG. 9 shows an example flow diagram of a method for implementing remotedistribution of neural networks, according to some example embodiments.

FIG. 10 shows an example flow diagram of a method for distribution ofneural networks to client devices over a network, according to someexample embodiments.

FIG. 11 shows an example flow diagram of a method generating a neuralnetwork model from another neural network model, according to someexample embodiments.

FIG. 12 shows an example architecture for implementing remotedistribution of neural networks, according to some example embodiments.

FIG. 13 shows an example user interface for triggering a remote modelconversion process, according to some example embodiments.

FIGS. 14A-14C illustrate example user interfaces for implementingmultistage neural network processing, according to some exampleembodiments.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, neural networks can perform image processing tasks, butdistribution of neural network models is problematic. In particular,sending models built for a specific operating environment of a clientdevice can waste network bandwidth. Further, storing differentlarge-format models on the chance that a user may use them canunnecessarily expend client device memory resources.

To this end, a native neural network (NNN, or “N3”) model can be trainedfor execution in an application. Different N3 models can be trained fordifferent tasks, such as image style domain transfer, imagesegmentation, object classification; as well as non-image-related neuralnet tasks, such as natural language processing (e.g., usingbidirectional long short-term memory (LSTM) neural networks).

One or more of the trained N3 models can be integrated and distributedwithin an application to a plurality of client devices, e.g., laptops,tablets, smartphones, desktops. In some embodiments, the application isdistributed with no N3 models included, and the N3 models are retrievedfrom a server upon the downloaded application being initiated on theclient device. The N3 model can be a custom light-weight neural networkmodel based on well-performing architectures, such as AlexNet 2012, asis known in the art. The N3 models are lightweight in that variousparameters have been modified for fast execution using less memory spaceon the client devices, many of which have limited memory and limitedprocessing power.

The application can include an N3 execution engine that is configured toapply an N3 model to input data using a processor of the client device,such as a central processing unit (CPU), to yield an output image, whichthen can be stored or published to a network. While the N3 models enablegood quality results, larger models often yield higher quality results.Further, some larger models, though they may have a larger computationalfootprint, can still be executed more quickly than an N3 because thelarger models have hardware acceleration support, (e.g., Apple® CoreML).

While the larger models have some advantages, each client device mayrequire a specific larger model format based on the client devicesoperating system (OS) (e.g., OSX, iOS, Windows, Linux, Android).Further, many client device that have the same OS may have differentprocessors and hardware capabilities. Thus, creating, transmitting, andmanaging different versions of models can create large overhead andresult in wasted server resources, bandwidth, and client-side resources.

To this end, a client device (e.g., an application of the client device)can implement a remote converter system that is configured to receive anN3 model, parse the model into layers and parameters, and create alarger format model based on the computational resources identified in agiven client device. For instance, an N3 model can be transmitted to aclient device, and a remote converter, in effect, disassembles the N3model and reassembles it block-by-block into a target neural network(TNN) model that is based on the that specific client device'scomputational resources (e.g., a GPU, hardware acceleration libraries,multi-core parallel or concurrent processing on a CPU).

Conversion of N3s into larger model formats can be performed whenspecific neural net tasks are requested by the user of the clientdevice. That is, the larger model formats are created on-the-fly, onlywhen a user requests them. Further, the application or remote converterengine can pre-convert a given N3 model into a larger format when itseems likely that a user may use it. For example, when a user navigatesto a certain user interface, the N3 model can be converted to the largerformat before a UI option is presented to the user for applying theimaging effect. In this way, when the user is presented with an option(e.g., UI button) to apply an image effect, the larger format model isready for use. Further, in those example embodiments, the larger formatmodel (or the N3 model) may generate a result using the newly convertedmodel so the user can immediately see the result upon selecting the UI.In this way, user experience is improved because the user does not haveto wait for a newer model to download from the server and also does notwait to see the result image. Further, by distribution via remoteconversion, network bandwidth and client device resources are saved.Further, by shifting model management to a remote device, versioning ofneural net models is greatly improved.

FIG. 1 shows a block diagram of an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network106. The messaging system 100 includes multiple client devices 102, eachof which hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via the network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed either by a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, a social network system 122, and a trainingsystem 123, in some example embodiments. The messaging serverapplication 114 implements a number of message-processing technologiesand functions, particularly related to the aggregation and otherprocessing of content (e.g., textual and multimedia content) included inmessages received from multiple instances of the messaging clientapplication 104. As will be described in further detail, the text andmedia content from multiple sources may be aggregated into collectionsof content (e.g., called stories or galleries). These collections arethen made available, by the messaging server application 114, to themessaging client application 104. Other processor- and memory-intensiveprocessing of data may also be performed server-side by the messagingserver application 114, in view of the hardware requirements for suchprocessing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3 ) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The training system 123 manages training of neural networks for specifictasks, such as image segmentation, image domain style transfer, objectclassification, natural language processing, audio-based sceneidentification, etc. The training system 123 may generate trained N3models for distribution and remote conversion on one or more clientdevices, as further discussed below.

The application server 112 is communicatively coupled to the databaseserver 118, which facilitates access to the database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, a remote converter 210,and a curation interface 208.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a Story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a geofilter or filter) tothe messaging client application 104 based on a geolocation of theclient device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, text, logos, animations, and sound effects. An exampleof a visual effect includes color overlaying. The audio and visualcontent or the visual effects can be applied to a media content item(e.g., a photo) at the client device 102. For example, the media overlayincludes text that can be overlaid on top of a photograph generated bythe client device 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. In another exampleembodiment, the annotation system 206 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the annotation system 206 associates the media overlay of ahighest-bidding merchant with a corresponding geolocation for apredefined amount of time.

In some example embodiments, the messaging client application 104further comprises a remote converter 210, which manages applying N3models to input images and remote conversion of the N3 models, asdiscussed in further detail below.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata 300 could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a Story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: values identifying one or more        content collections (e.g., “stories”) with which a particular        content item in the message image payload 406 of the message 400        is associated. For example, multiple images within the message        image payload 406 may each be associated with multiple content        collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral), according to some example embodiments.

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone example, an ephemeral message 502 is viewable by a receiving userfor up to a maximum of 10 seconds, depending on the amount of time thatthe sending user specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504. The ephemeral message story 504 has anassociated story duration parameter 508, a value of which determines atime duration for which the ephemeral message story 504 is presented andaccessible to users of the messaging system 100. The story durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message story 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the story durationparameter 508 when performing the setup and creation of the ephemeralmessage story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring (via thestory timer 514) in terms of the story duration parameter 508.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104.

FIG. 6 shows example functional internal components of a remoteconverter 210, according to some example embodiments. As illustrated,the remote converter 210 comprises an interface engine 605, a nativeneural network (N3) engine 610, a target neural network (TNN) engine615, a conversion engine 620, and a mapping engine 625. The interfaceengine 605 manages generation or identification of an input image formodification by one or more neural networks. Further, the interfaceengine 605 can manage triggering a neural network conversion process inresponse to a certain application event occurring (e.g., navigation to apre-specified user interface, upon activation of the messaging clientapplication 104, etc.). The N3 engine 610 is configured to apply atrained N3 model to input data (e.g., an image, an audio file) togenerate output data for storage or distribution over a network. Thetarget neural network (TNN) engine 615 is configured to apply remotelygenerated target neural network (TNN) model to an input data (e.g., animage, an audio file) to generate output data. The conversion engine 620manages receiving an instruction to convert a specified N3 model to aTNN model, e.g., for application to input data. In some exampleembodiments, the conversion engine 620 interfaces with other engines ofthe remote converter 210 using an application programming interface(API) to allow easy programming and distribution of newly configured N3models. The mapping engine 625 is configured to manage parsing layersand identifying model-specific information of an N3 model and map andconvert the parsed information to the format used by the TNN model. Insome example embodiments, the mapping engine 625 is integrated withinthe conversion engine 620. Further in some example embodiments, themapping engine 625 is a standalone object within the remote converter210

FIG. 7 shows an example flow diagram of a method 700 for implementingremote distribution of neural networks, according to some exampleembodiments. At operation 705, the interface engine 605 generates animage on the client device 102. For example, at operation 705, theinterface engine 605 uses an image sensor of the client device 102 togenerate an image, and stores the image to local memory of the clientdevice 102. As an additional example, at operation 705, the interfaceengine 605 identifies an image that has been pre-generated elsewhere(e.g., by another engine in the messaging client application 104, byanother application executing on the client device 102, or an imagedownloaded from a network server). At operation 710, the interfaceengine 605 generates a request that initiates an image processing task.For example, a user may select a user interface (UI) button to apply animaging task (e.g., image domain style transfer) to the image generatedat operation 705. In response to the request, at operation 715, theinterface engine 605 identifies the N3 model configured to perform therequested task. At operation 720, the interface engine 605 determinescomputational resources of the client device 102. For example, atoperation 720, the interface engine 605 may determine a native screensize of the client device 102, bandwidth quality, processor type, GPUavailability, and hardware acceleration support.

In some example embodiments, if the client device 102 does not havehardware acceleration support and has limited computational resources,the interface engine 605 generates an instruction for the N3 engine 610to apply the N3 model. For example, if at operation 720 the interfaceengine 605 determines that the client device 102 does not have a GPUand/or hardware acceleration support, then at operation 723 the N3engine 610 applies the N3 model to the input image using the CPU ofclient device 102. The resulting image can then be stored at operation735.

Alternatively, if the interface engine 605 determines that the clientdevice 102 has enhanced capabilities for execution of larger formatneural network models (e.g., GPU, hardware acceleration platform), theinterface engine 605 transmits an instruction to the conversion engine620 to convert the N3 model for the specified task to a TNN model basedon the client device's resources. For example, at operation 720, theinterface engine 605 may determine that a hardware acceleration platform(e.g., CoreML) is enabled on the client device 102. In response, theinterface engine 605 may generate an instruction that specifies an inputN3 model, a TNN model to be generated, and other parameters, such asimage size. The instruction is transmitted to an API of the conversionengine 620, which then generates the TNN model in operation 725.

At operation 730, the TNN engine 615 applies the newly generated TNNmodel to the image to create a modified image. At operation 735, theinterface engine 605 stores the modified image in memory and optionallypublishes it to a network site as an ephemeral message.

As discussed above, by implementing a remote converter 210, largerformat neural network models can be generated using on-the-flyconversion. FIGS. 8 and 9 discuss different approaches for initiatingconversion in response to different application events occurring.

FIG. 8 shows an example flow diagram of a method 800 for implementingremote distribution of neural networks, according to some exampleembodiments. In the method 800, the N3 model may have been downloadedwith the messaging client application 104. If it is determined that theclient device 102 has large memory resources, one or more TNN modelsthat are most frequently used by users can be generated in thebackground using the example method 800. In some example embodiments,client devices report usage data to the application server 112 and theapplication server transmits an instruction to the remote converter 210of which models to convert based on frequency statistics from the usagedata.

At operation 805, the operating system (OS) of the client device 102initiates the messaging client application 104. At operation 810, inresponse to the messaging client application 104 being made active, theconversion engine 620 initiates conversion of a N3 model to a TNN modelas a background process on the client device 102. At operation 815, theconversion engine 620 stores the newly generated target neural networkmodel in memory of the client device 102. Although only one model isdiscussed here in the example method 800, one of ordinary skill in theart appreciates that multiple models each configured for different tasksmay be likewise converted and stored as background processes of theclient device 102.

At operation 820, the interface engine 605 receives a request to applyan image effect. The image effect may be a task for which the N3 modeland the TNN model are trained. At operation 825, the TNN engine 615applies the newly generated TNN model to the specified image to generatea modified image, which can then be locally stored and/or distributedover a network as an ephemeral message.

FIG. 9 shows an example flow diagram of a method 900 for implementingremote distribution of neural networks, according to some exampleembodiments. At operation 905, the operating system of the client device102 initiates the application, as discussed above. At operation 910, themessaging client application 104 displays various user interfaces inresponse to the user's navigation inputs. For example, the user may viewa primary newsfeed area of the messaging client application 104 and thenpress an image generation button which allows the user to take a pictureof him or herself (e.g., a “selfie”), which can then be displayed on thedisplay device of the client device 102. At operation 915, to a pre-setUI control being activated, a conversion process is initiated whichconverts one or more N3 models to corresponding TNN models. For example,in response to the picture being displayed on the client device, theconversion engine 620 triggers conversion of the N3 model to the TNNmodel. In this way, if the user chooses to apply an effect for which theN3/TNN model is trained, the models can immediately be implementedwithout the user experiencing processing delay. Although in the exampleof method 900 the trigger event is an image or user interface beingdisplayed, one of ordinary skill in the art appreciates that conversioncould be triggered by a navigation path of the user through themessaging client application 104, or other UI-based events such as thescrolling of icons that are configured to initiate different imageeffects, as discussed in further detail below

At operation 920, the interface engine 605 shows a UI element that isconfigured to apply an image effect for which the N3 model and newlygenerated TNN model are configured to apply. At operation 925, theinterface engine 605 receives a request to apply an image effect to theimage. At operation 930, the TNN engine 615 applies the pre-emptivelycreated TNN model to the image to generate a modified image for displayand optional publication to the a network. Further, according to someexample embodiments, both the TNN model and the modified image arepre-generated before the user has an option to view the image effect. Inthis way, when the user selects the UI button to view the modifiedimage, the modified image is immediately displayed to the user withouthaving to wait for the TNN model to be created or downloaded from thenetwork.

FIG. 10 shows an example flow diagram of a method 1000 for distributionof neural networks to client devices over a network, according to someexample embodiments. At operation 1005, the interface engine 605receives a new N3 model from a network server, e.g., from the trainingsystem 123 on application server 112. At operation 1010, in response toreceiving the new N3 model, the interface engine 605 generates aconversion instruction, as discussed above. At operation 1015, theconversion engine 620 converts the N3 model to the TNN model. Atoperation 1020, the interface engine 605 receives a request to apply animage effect to an image. At operation 1025, the TNN model is applied tothe image to generate a modified image.

FIG. 11 shows an example flow diagram of a method 1100 generating a TNNmodel from a N3 model, according to some example embodiments. Atoperation 1105, the conversion engine 620 receives an API request toinitiate a conversion from a N3 model to a specified TNN model specifiedin the request. The API request may come from another engine within theremote converter 210 or another system executing on the client device102, such as another application.

At operation 1110, the mapping engine 625 parses the N3 model toidentify elements to map to elements of a TNN model, as discussed infurther detail below with reference to FIG. 12 .

At operation 1115, the mapping engine 625 sets different parameters thanthe N3 format uses. For example, the N3 model may use float16 values andthe TNN model may use the more precise float32 or float64 values; thusat operation 1115 the TNN model is configured to use more preciseformat. Likewise, weights of the N3 model may be quantized or otherwiselimited to 16 bits to preserve the small footprint of the N3 model;accordingly, at operation 1115 the TNN model is configured to use largerbit sized weights. Further, in some example embodiments, TNN modelsrequire declaration of certain values before the model is generated. Forexample, the input image size (the image generated at operation 705 ofFIG. 7 ) can be specified at operation 1115.

At operation 1120, the conversion engine 620 converts each of theelements to the TNN format based on the mapping. In some exampleembodiments, the TNN model is created at operation 1120 when all theelements are converted using the mappings. However, according to someexample embodiments, some TNN models (e.g., CoreML models) still must becompiled for execution via hardware acceleration. Thus, as according tosome example embodiments, at operation 1125 the conversion engine 620compiles the converted elements to generate the TNN model, according tosome example embodiments.

Examples of code for performing method 1100 include:

:::::::::::::::::::EXAMPLE 1 - BEGIN:::::::::::::::::: 1 // Setup inputand output params for the model conversion like dimensions/layer names 2InputParams inputParams(FastConvert::TensorShape(224, 224, 3) /* inputdimension */, “input_layer_name”); 3 OutputParams outputParams({“out1”,“out2”} /* output names */); 5 // Create model converter for yourspecific purpose. Ex: on ios you'd want libdnn->coreml 6 ModelConverterconverter(FastConvert::Backend::LIBDNN, FastConvert::Backend::COREML); 78 // Convert the model 9 const std::string N3modelPath =“/path/to/N3.model” 10 std::string outputPath =“/path/to/output/TNN.coreml” 11 converter.Convert(N3modelPath,outputPath, inputParams, OutputParams); 12 13 // Perform any furtherprocessing as required on the TNN output model 14 std::stringcoreMLCompiled = FastConvert::CompileModel(outputPath); 15 16 // Runinference using FastConvert and the converted model 17FastConvert::FastConvert network; 18 network.LoadModel(coreMLCompiled,“input_layer_name”, {“out1”, “out2”},FastConvert::Backend::Type::COREML); 19 20 // Wrap input image inFastConvert tensor 21 float *my_data_ptr = input.get_ptr( ); 22FastConvert::TensorShape data_shape(input.get_dimension( )); 23 autotensor = FastConvert::Tensor::Wrap(my_data_ptr, data_shape); 24 25 //Run inference 26 auto outputTensorMap = network.Predict(tensor); 27 28// Access inference result 29 auto result1 = outputTensorMap[“out1”]; 30auto result2 = outputTensorMap[“out2”]; :::::::::::::::::::EXAMPLE 1 -END::::::::::::::::::

:::::::::::::::::::EXAMPLE 2 - BEGIN::::::::::::::::::: 1 #include<FastConvert/FastConvert.h> 2 void ConvertModel(std::string& inputPath,std::string& destPath) 3 { 4 FastConvert::ModelConversion::InputParamsinputParams(FastConvert::TensorShape(168, 96, 3), “image”); 5 // Tosupport models with multiple outputs, we need to specify the outputnames in the format of std::v 6FastConvert::ModelConversion:OutputParams outputParams({“prob”}); 7FastConvert::ModelConversion::ModelConverterconverter(FastConvert::Backend::N3, FastConvert::Backend::COREML); 8 //Input path is the path to the model file, e.g. /tmp/portrait.dnn 9 //DestPath is the path of the directory we want to save the model to, e.g/tmp/outputModel. 10 converter.Convert(inputPath, destPath, inputParams,outputParams); :::::::::::::::::::EXAMPLE 2 - END::::::::::::::::::

FIG. 12 shows an example architecture for implementing remotedistribution of neural networks, according to some example embodiments.As discussed above, the interface engine 605 can generate an instructionto convert an N3 model to a TNN model. The instruction is received by anAPI 1205 of the conversion engine 620. The conversion engine 620 usesthe input path 1215 to locate the N3 model 1210. The mapping engine 625is configured to parse the N3 model 1210 into its constituent parts,such as layers, ordering of layers, tensor size, N3 labels, etc. Themapping engine 625 maps each of the constituent parts of the N3 format1220 to the TNN format 1225. The mapping can support mapping fordifferent layers used in neural networks, including, for example:convolution, deconvolution, depth deconvolution, Relu, Prelu, Tanh,Pool, Eltwise, concatenation, Flatten, Permute, Reshape, Const,InnerProduct, Softmax, Batchnorm, Cropping nodes, Unpooling, Recurrentnodes, gated recurrent unit (GRU), LSTM layers, attention layers, andothers.

The parameters can include data types used (e.g., size of floats),dimensions of the layers, ordering of the layers, additional informationrequired by the TNN model 1235 (e.g., image size), and encryption andcompression schemes. The weights are weights of the connections betweenlayers of the deep neural networks. They can be generated via trainingon the server side for different tasks.

The conversion engine 620 can then construct the TNN model 1235 andstore it in an output path 1230 specified by the instruction. The TNNmodel 1235 is executable on an operating system of the client device102, such as operating system 1502 of FIG. 15 . The operating system1502 has libraries 1240 that may be used when the TNN model 1235 isbeing created or may be called by the TNN engine 615 when the TNN model1235 is being applied to the input image.

Further, as new N3 models are created on the server side, they may bepublished and converted remotely on a plurality of different deviceshaving different operating environments. For example, as illustrated inFIG. 12 , a new N3 model 1250 can be transmitted over a networkefficiently to the N3 engine 610 via the interface engine 605. In someexample embodiments, the interface engine 605 generates an instructionto convert the new N3 model 1250 into the TNN format 1225 upon receivingthe new N3 model 1250. Further in other example embodiments, the new N3model 1250 may be converted when it becomes likely that the user willuse the image effect for which the new N3 model 1250 was trained.

FIG. 13 shows an example user interface 1300 for triggering a remotemodel conversion process, according to some example embodiments. Theuser interface 1300 displays an image 1305 (e.g., a selfie) that depictsthe user of the client device 102. User interface 1300 further displaysa plurality of UI buttons 1310, —B1, B2, B3, B4, etc.—each of which canbe configured to apply an image effect to the image 1305 of the user.Each of the image effects of the different UI buttons 1310 cause themessaging client application 104 to apply a neural network trained for aspecific task to the image 1305 of the user. As discussed above,conversion and application of the neural network models to the image maybe preemptively performed to enhance the users experience. For example,after the image 1305 generates the image, upon the image being displayedwithin the user interface 1300, the interface engine 605 may generatemultiple instructions to convert all corresponding N3 models to TNNmodels.

Alternatively, in some embodiments, the conversion instruction istriggered in response to an increased likelihood that the user willselect a given UI button of the plurality of UI buttons 1310. Forexample, the plurality of UI buttons 1310 may be operable as an iconcarousel that can be swiped from right to left to display additional UIbuttons that were previously off of the screen, e.g., B5, B6, B7. Inresponse to receiving a scroll of the carousel, the interface engine 605can trigger a model conversion instruction for a UI button that has yetto be displayed (e.g., B11). Thus, as the user scrolls a plurality of UIbuttons 1310, a TNN model for the yet to be displayed button can bepre-generated and/or pre-applied to the image 1305, such that when theuser selects the UI button, the resultant image is immediately displayedwithin the user interface 1300.

FIGS. 14A-C illustrate example user interfaces for implementingmultistage neural network processing, according to some exampleembodiments. As illustrated in FIG. 14A, image 1400 is an example of animage captured at operation 705 of FIG. 7 . The image 1400 depicts asmiling girl holding her hat and a tasty beverage. A user (e.g., thegirl or another person holding a mobile phone taking a picture of thegirl) may have selected button 1405 to initiate styling of the image1400. An image mask may be required to perform the styling (e.g., theimage mask is a mouth mask that labels pixels depicting the mouth areaof the girl).

Responsive to selection of the button 1405, a TNN model is generated andapplied to the image 1400, as discussed above. FIG. 14B shows asegmented image 1403, which has been generated by performing imagesegmentation on image 1400 using the TNN model. The image 1403 denotesdifferent areas of the image, including for example a hat area 1407(labeled “1”), skin areas that are not part of the face 1410 (labeled“2”), a face area 1415 (labeled “3”), and a clothes area 1420 (labeled“4”). The different label values may be included as channel data foreach pixel (e.g., a fourth channel in addition to RGB (Red/Blue/Green)channels). Further, the label values may be stored as a separate imagehaving the same height and width as image 1403.

FIG. 14C shows an example modified image 1425 which has undergone styletransfer from a smile style to a frown style using an image mask (e.g.,an eye area image mask, a mouth area image mask). In particular, forexample, a second TNN configured to perform image style transfer can beconverted and applied to the face area 1415 using the image mask. Afterthe modified image 1425 is generated, it can be published to a network(e.g., published to a social network as an ephemeral message 502). Insome example embodiments, both the TNN model to perform imagesegmentation and the TNN model to perform image style domain transferare generated upon the image 1400 being displayed on the client device102. Further, according to some example embodiments, both of the TNNmodels are generated on the fly only when the user selects button 1405,thereby saving network bandwidth and computational resources of theclient device 102.

FIG. 15 is a block diagram illustrating an example software architecture1506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 15 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1506 may execute on hardwaresuch as a machine 1600 of FIG. 16 that includes, among other things,processors, memory, and I/O components. A representative hardware layer1552 is illustrated and can represent, for example, the machine 1600 ofFIG. 16 . The representative hardware layer 1552 includes a processingunit 1554 having associated executable instructions 1504. The executableinstructions 1504 represent the executable instructions of the softwarearchitecture 1506, including implementation of the methods, components,and so forth described herein. The hardware layer 1552 also includes amemory/storage 1556, which also has the executable instructions 1504.The hardware layer 1552 may also comprise other hardware 1558.

In the example architecture of FIG. 15 , the software architecture 1506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1506may include layers such as an operating system 1502, libraries 1520,frameworks/middleware 1518, applications 1516, and a presentation layer1514. Operationally, the applications 1516 and/or other componentswithin the layers may invoke API calls 1508 through the software stackand receive a response in the form of messages 1512. The layersillustrated are representative in nature, and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1502 may manage hardware resources and providecommon services. The operating system 1502 may include, for example, akernel 1522, services 1524, and drivers 1526. The kernel 1522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1524 may provideother common services for the other software layers. The drivers 1526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1520 provide a common infrastructure that is used by theapplications 1516 and/or other components and/or layers. The libraries1520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1502 functionality (e.g., kernel 1522,services 1524, and/or drivers 1526). The libraries 1520 may includesystem libraries 1544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1520 may include API libraries 1546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1520 may also include a wide variety ofother libraries 1548 to provide many other APIs to the applications 1516and other software components/modules.

The frameworks/middleware 1518 provide a higher-level commoninfrastructure that may be used by the applications 1516 and/or othersoftware components/modules. For example, the frameworks/middleware 1518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1518 may provide a broad spectrum of other APIsthat may be utilized by the applications 1516 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1502 or platform.

The applications 1516 include built-in applications 1538 and/orthird-party applications 1540. Examples of representative built-inapplications 1538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1540 may invoke the API calls 1508 provided bythe mobile operating system (such as the operating system 1502) tofacilitate functionality described herein.

The applications 1516 may use built-in operating system functions (e.g.,kernel 1522, services 1524, and/or drivers 1526), libraries 1520, andframeworks/middleware 1518 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1514. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1616 may be used to implement modules or componentsdescribed herein. The instructions 1616 transform the general,non-programmed machine 1600 into a particular machine 1600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1616, sequentially or otherwise, that specify actions to betaken by the machine 1600. Further, while only a single machine 1600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610, memory/storage 1630, andI/O components 1650, which may be configured to communicate with eachother such as via a bus 1602. The memory/storage 1630 may include amemory 1632, such as a main memory, or other memory storage, and astorage unit 1636, both accessible to the processors 1610 such as viathe bus 1602. The storage unit 1636 and memory 1632 store theinstructions 1616 embodying any one or more of the methodologies orfunctions described herein. The instructions 1616 may also reside,completely or partially, within the memory 1632, within the storage unit1636, within at least one of the processors 1610 (e.g., within theprocessor cache memory accessible to processors 1612 or 1614), or anysuitable combination thereof, during execution thereof by the machine1600. Accordingly, the memory 1632, the storage unit 1636, and thememory of the processors 1610 are examples of machine-readable media.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine 1600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1650 may include many other components that are not shown inFIG. 16 . The I/O components 1650 are grouped according to functionalitymerely for simplifying the following discussion, and the grouping is inno way limiting. In various example embodiments, the I/O components 1650may include output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentcomponents 1660, or position components 1662 among a wide array of othercomponents. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672, respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1616 forexecution by the machine 1600, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1616. Instructions 1616 may betransmitted or received over the network 1680 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1600 thatinterfaces to a network 1680 to obtain resources from one or more serversystems or other client devices 102. A client device 102 may be, but isnot limited to, a mobile phone, desktop computer, laptop, PDA,smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1680.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1680 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1680 may include a wireless or cellular network,and the coupling 1682 may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EMPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1616 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1616. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1616 (e.g., code) forexecution by a machine 1600, such that the instructions 1616, whenexecuted by one or more processors 1610 of the machine 1600, cause themachine 1600 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1612 ora group of processors 1610) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1600) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1610. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1612configured by software to become a special-purpose processor, thegeneral-purpose processor 1612 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1612 or processors 1610, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1610 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1610 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1610. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1612 or processors1610 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1610or processor-implemented components. Moreover, the one or moreprocessors 1610 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1600including processors 1610), with these operations being accessible via anetwork 1680 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1610, not only residing within asingle machine 1600, but deployed across a number of machines 1600. Insome example embodiments, the processors 1610 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1610 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1612) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1600.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1610may further be a multi-core processor 1610 having two or moreindependent processors 1612, 1614 (sometimes referred to as “cores”)that may execute instructions 1616 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: detecting, from a device, atriggering event of a neural network conversion process associated withan image effect; identifying, by one or more hardware processors, anative neural network model associated with the image effect; convertingthe native neural network model to a target neural network model basedon computational resources associated with the device; in response toconverting the native neural network model to a target native neuralnetwork model, causing display of user interface element associated withthe image effect on a user interface of the device; and generating amodified image by applying the target neural network model to an image.2. The method of claim 1, wherein the triggering event of the neuralnetwork conversion process comprises an application event thatcorresponds to any one of: navigation to a pre-specified user interfaceor activation of a messaging client application associated with thedevice.
 3. The method of claim 1, comprising: determining the targetneural network model based on an operating system associated with thedevice having libraries for parallel execution of neural networks on aplurality of hardware processors.
 4. The method of claim 3, wherein thetarget neural network model is associated with input parameters that arenot included in the native neural network model.
 5. The method of claim1, comprising: identifying computational hardware of the device; andmodifying a size of the image to an input image size based on anidentified computational hardware of the device.
 6. The method of claim1, comprising: specifying a plurality of input parameters of the targetneural network model; and compiling the target neural network modelusing the plurality of input parameters.
 7. The method of claim 6,wherein the plurality of input parameters includes a first inputparameter corresponding to an input image size of the image.
 8. Themethod of claim 1, wherein the computational resources associated withthe device comprises one or more of: a screen size, bandwidth quality,GPU availability, one or more hardware acceleration libraries,multi-core parallel processing capability on a CPU, and multi-coreconcurrent processing capability on the CPU.
 9. The method of claim 1,comprising: receiving usage data from the device; and identifying thenative neural network model associated with the image effect based onthe usage data.
 10. The method of claim 9, wherein the usage datacomprises frequency statistics of neural network model conversion.
 11. Asystem comprising: a memory storing instructions; and one or morehardware processors communicatively coupled to the memory and configuredby the instructions to perform operations comprising: detecting, from adevice, a triggering event of a neural network conversion processassociated with an image effect; identifying a native neural networkmodel associated with the image effect; converting the native neuralnetwork model to a target neural network model based on computationalresources associated with the device; in response to converting thenative neural network model to a target native neural network model,causing display of user interface element associated with the imageeffect on a user interface of the device; and generating a modifiedimage by applying the target neural network model to an image.
 12. Thesystem of claim 11, wherein the triggering event of the neural networkconversion process comprises an application event that corresponds toany one of: navigation to a pre-specified user interface or activationof a messaging client application associated with the device.
 13. Thesystem of claim 11, wherein the operations comprise: determining thetarget neural network model based on an operating system associated withthe device having libraries for parallel execution of neural networks ona plurality of hardware processors.
 14. The system of claim 13, whereinthe target neural network model is associated with input parameters thatare not included in the native neural network model.
 15. The system ofclaim 11, wherein the operations comprise: identifying computationalhardware of the device; and modifying a size of the image to an inputimage size based on an identified computational hardware of the device.16. The system of claim 11, wherein the operations comprise: specifyinga plurality of input parameters of the target neural network model; andcompiling the target neural network model using the plurality of inputparameters.
 17. The system of claim 16, wherein the plurality of inputparameters includes a first input parameter corresponding to an inputimage size of the image.
 18. The system of claim 11, wherein thecomputational resources associated with the device comprises one or moreof: a screen size, bandwidth quality, GPU availability, one or morehardware acceleration libraries, multi-core parallel processingcapability on a CPU, and multi-core concurrent processing capability onthe CPU.
 19. The system of claim 11, wherein the operations comprise:receiving usage data from the device; and identifying the native neuralnetwork model associated with the image effect based on the usage data.20. A non-transitory machine-readable storage medium comprisinginstructions that, when executed by a processing device, cause theprocessing device to perform operations comprising: detecting, from adevice, a triggering event of a neural network conversion processassociated with an image effect; identifying a native neural networkmodel associated with the image effect; converting the native neuralnetwork model to a target neural network model based on computationalresources associated with the device; in response to converting thenative neural network model to a target native neural network model,causing display of user interface element associated with the imageeffect on a user interface of the device; and generating a modifiedimage by applying the target neural network model to an image.